Development of Lifelong SLAM System for Service Robots using Deep Semantic Information

本研究旨在通过解决两个主要问题，为动态环境下的移动机器人开发终身语义导航系统。2.对室内环境中日常物体的语义理解，用于长期绘图和导航；基于多层语义地图的不确定目标搜索、检测和探索规划。在模拟和真实环境下进行了数据采集，并对基于搜索的优化规划进行了测试。该项目旨在通过提供对日常物体及其与位置的关联的深入理解，以及开发终身学习的本体，增强服务机器人的人机交互。该系统可以提高动态环境下搜索和探索等任务的性能。

This study aims to develop a lifelong semantic navigation system for mobile robots in dynamic environments by addressing two main problems, 1. Semantic understanding of everyday objects in indoor environments for long-term mapping and navigation, and 2. Planning under uncertainty for object search, detection, and exploration using multi-layered semantic maps. Data collection in simulated and real environments and tests for search-based optimized planning has been conducted. The project aims to enhance human-robot interaction in service robots by providing deep understanding of daily objects and their associations with locations, and developing an ontology for lifelong learning. This system can improve performance in tasks like search and exploration in dynamic settings.